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Оглавление - Neural networks guide
Unleash the power of Neural Networks: the complete guide to understanding, Implementing AI
- Introduction
- Understanding Neural Networks: An Overview
- What are Neural Networks?
- Why are Neural Networks Important?
- How Neural Networks Mimic the Human Brain
- Part I: Getting Started with Neural Networks
- The Basics of Artificial Neural Networks
- Components of a Neural Network
- Activation Functions
- Neural Network Architectures
- Training Neural Networks
- Preparing Data for Neural Networks
- Data Representation and Feature Scaling
- Data Preprocessing Techniques
- Handling Missing Data
- Dealing with Categorical Variables
- Part II: Building and Training Neural Networks
- Feedforward Neural Networks
- Structure and Working Principles
- Implementing a Feedforward Neural Network
- Fine-tuning the Model
- Convolutional Neural Networks (CNNs)
- Introduction to CNNs
- Understanding Convolution and Pooling Layers
- Building and Training a CNN
- Applications of CNNs
- Recurrent Neural Networks (RNNs)
- Introduction to RNNs
- Understanding the Role of Recurrence
- Training and Utilizing RNNs
- Applications of RNNs
- Part III: Enhancing Neural Network Performance
- Overcoming Overfitting and Underfitting
- Understanding Overfitting and Underfitting
- Regularization Techniques
- Dropout and Early Stopping
- Hyperparameter Tuning
- Importance of Hyperparameters
- Strategies for Hyperparameter Tuning
- Grid Search and Random Search
- Optimizing Neural Networks
- Gradient Descent and Optimization Algorithms
- Batch Normalization
- Weight Initialization Techniques
- Transfer Learning
- Part IV: Practical Applications of Neural Networks
- Computer Vision
- Image Classification
- Object Detection
- Image Segmentation
- Face Recognition
- Natural Language Processing
- Text Classification
- Sentiment Analysis
- Language Generation
- Machine Translation
- Recommender Systems
- Collaborative Filtering
- Content-Based Filtering
- 3Hybrid Approaches
- Conclusion
- The Future of Neural Networks
- Recent Advancements and Trends
- Ethical Considerations
- Opportunities and Challenges Ahead
- Glossary
- Appendix: Implementing Neural Networks with Python
- References